The HaLer project aims to develop solutions for flexible and adaptive human-machine interaction (MMI). The developed methods should allow a system to react and adapt flexibly to human behavior even in the case of minor changes in the situation. For this purpose, deviations in human behavior based on behavioral data and EEG data are to be detected. Furthermore, the combined use of behavioral data and EEG data should make it possible to predict upcoming sequences of action.
In order to detect deviations in the human's actions, both the human's movements by means of motion tracking and intrinsic motivations and evaluations of current situations are analyzed using EEG data. The data will be analyzed using learning methods developed by the Robotics Group of the University of Bremen, which has great expertise in the development of adaptive and robust learning architectures and learning methods. The focus is on methods that do not require large computing capacities. Thus, the methods can be applied in scenarios with limited resources, such as space missions. The focus of DFKI's work is on the development of test environments and the evaluation of methods developed by the robotics group at the University of Bremen.